Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis.

Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.

A new paradigm for smart packaging: A dual-channel freshness monitoring platform based on aerogels of sodium alginate-anthocyanin complex with high colorimetric sensitivity and stability.

Global seafood consumption is estimated at 156 million tons annually, with an economic loss of >25 billion euros annually due to marine fish spoilage. In contrast to traditional smart packaging which can only roughly estimate food freshness, an intelligent platform integrating machine learning and smart aerogel can accurately predict remaining shelf life in food products, reducing economic losses and food waste. In this study, we prepared aerogels based on anthocyanin complexes that exhibited excellent environmental responsiveness, high porosity, high color-rendering properties, high biocompatibility, high stability, and irreversibility. The aerogel showed excellent indication properties for rainbow trout and proved suitable for fish storage environments. Among the four machine learning models, the radial basis function neural network and backpropagation network optimized by genetic algorithm demonstrated excellent monitoring performance. Also, the two-channel dataset provided more comprehensive information and superior descriptive capability. The three-layer structure of the monitoring platform provided a new paradigm for intelligent and sophisticated food packaging. The results of the study might be of great significance to the food industry and sustainable development.

Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.

At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorithms were used for the first time to develop a multi-objective model that can simultaneously predict the shelf-life of five marine fish species at multiple storage temperatures using 14 features such as species, temperature, total viable count, K-value, total volatile basic­nitrogen, sensory and E-nose-GC-Ms/Ms. as inputs. Among them, the radial basis function model performed the best, and the absolute errors of all test samples were <0.5. With the optimal model as the base layer, a real-time prediction platform was developed to meet the needs of practical applications. This study successfully realized multi-objective real-time prediction with accurate prediction results, providing scientific basis and technical support for food safety and quality.

A review of microplastic transport in coastal zones.

Transport of microplastics (MPs) in coastal zones is influenced not only by their own characteristics, but also by the hydrodynamic conditions and coastal environment. In this article, we first summarized the source, distribution and abundance of MPs in coastal zones around the world through the induction of in-situ observation literature, and then comprehensively reviewed the different transports of MPs in coastal zones, including sedimentation, vertical mixing, resuspension, drift and biofouling. Afterwards, we conducted a comparative analysis of relevant experimental literature, and found that the current experimental research on microplastic transport mainly focused on the settling velocity under static water and the transport distribution under dynamic water. Based on the relevant literature on numerical simulation of microplastic transport in coastal zones, it was also found that the Euler-Lagrange method is the most widely used. The main influencing factor in the Euler method is hydrodynamic, while the Lagrange method and Euler-Lagrange method is hydrodynamic and microplastic particle characteristics. Tides in hydrodynamics are mentioned the most frequently, and the role of turbulence in almost all the literature. The density of MPs is the most influencing factor on transport results, followed by size, while shape is only studied in small-scale models. Some literature has also found that the influence of biofilms is mainly reflected in the changes in the density and size of MPs.

Chromatin attachment to the nuclear matrix represses hypocotyl elongation in Arabidopsis thaliana.

The nuclear matrix is a nuclear compartment that has diverse functions in chromatin regulation and transcription. However, how this structure influences epigenetic modifications and gene expression in plants is largely unknown. In this study, we show that a nuclear matrix binding protein, AHL22, together with the two transcriptional repressors FRS7 and FRS12, regulates hypocotyl elongation by suppressing the expression of a group of genes known as SMALL AUXIN UP RNAs (SAURs) in Arabidopsis thaliana. The transcriptional repression of SAURs depends on their attachment to the nuclear matrix. The AHL22 complex not only brings these SAURs, which contain matrix attachment regions (MARs), to the nuclear matrix, but it also recruits the histone deacetylase HDA15 to the SAUR loci. This leads to the removal of H3 acetylation at the SAUR loci and the suppression of hypocotyl elongation. Taken together, our results indicate that MAR-binding proteins act as a hub for chromatin and epigenetic regulators. Moreover, we present a mechanism by which nuclear matrix attachment to chromatin regulates histone modifications, transcription, and hypocotyl elongation.

Preparation of multifunctional hydrogels based on co-pigment-polysaccharide complexes and establishment of a machine learning monitoring platform.

Economic loss due to fish spoilage exceeds 25 billion euros every year. Accurate and real-time monitoring of the freshness of fish can effectively cut down economic loss and food wastage. In this study, a dual-functional hydrogel based on sodium alginate-co-pigment complex with volatile antibacterial and intelligent indication was prepared and characterized. The characterization results indicated that the sodium alginate-co-pigment complex successfully improved the stability and color development ability of blueberry anthocyanins and bilberry anthocyanins at different temperatures and pH. The double cross-linking network inside the hydrogel conferred it with excellent mechanical properties. During rainbow trout storage, the hydrogel indicated a color difference of 73.55 on the last day and successfully extended the shelf-life of rainbow trout by 2 days (4 °C). Additionally, four dual-channel monitoring models were constructed using machine learning. The validation error of the genetic algorithm back propagation model (GA-BP) was only 5.6e-3, indicating that GA-BP can accurately monitor the freshness of rainbow trout. The rainbow trout real-time monitoring platform built based on GA-BP model can monitor the freshness of rainbow trout in real time through the images uploaded by users. The results of this study have broad applicability in the food industry, environmental conservation, and economic sustainability.

Targeting of the CD161 inhibitory receptor enhances T-cell-mediated immunity against hematological malignancies.

ABSTRACT: The CD161 inhibitory receptor is highly upregulated by tumor-infiltrating T cells in multiple human solid tumor types, and its ligand, CLEC2D, is expressed by both tumor cells and infiltrating myeloid cells. Here, we assessed the role of the CD161 receptor in hematological malignancies. Systematic analysis of CLEC2D expression using the Cancer Cell Line Encyclopedia revealed that CLEC2D messenger RNA was most abundant in hematological malignancies, including B-cell and T-cell lymphomas as well as lymphocytic and myelogenous leukemias. CLEC2D protein was detected by flow cytometry on a panel of cell lines representing a diverse set of hematological malignancies. We, therefore, used yeast display to generate a panel of high-affinity, fully human CD161 monoclonal antibodies (mAbs) that blocked CLEC2D binding. These mAbs were specific for CD161 and had a similar affinity for human and nonhuman primate CD161, a property relevant for clinical translation. A high-affinity CD161 mAb enhanced key aspects of T-cell function, including cytotoxicity, cytokine production, and proliferation, against B-cell lines originating from patients with acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and Burkitt lymphoma. In humanized mouse models, this CD161 mAb enhanced T-cell-mediated immunity, resulting in a significant survival benefit. Single cell RNA-seq data demonstrated that CD161 mAb treatment enhanced expression of cytotoxicity genes by CD4 T cells as well as a tissue-residency program by CD4 and CD8 T cells that is associated with favorable survival outcomes in multiple human cancer types. These fully human mAbs, thus, represent potential immunotherapy agents for hematological malignancies.

Spatial transcriptomics defines injury-specific microenvironments in the adult mouse kidney and novel cellular interactions in regeneration and disease.

Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, and injury-invoked inflammation and fibrosis. Deciphering molecular pathways and cellular interactions driving these processes is challenging due to the complex renal architecture. Here, we applied single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics revealed injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicted Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis revealed cellular microenvironments resembling early tertiary lymphoid structures and identified associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease.

Metagenomic next-generation sequencing for detecting lower respiratory tract infections in sputum and bronchoalveolar lavage fluid samples from children.

Lower respiratory tract infections are common in children. Bronchoalveolar lavage fluid has long been established as the best biological sample for detecting respiratory tract infections; however, it is not easily collected in children. Sputum may be used as an alternative yet its diagnostic accuracy remains controversial. Therefore, this study sought to evaluate the diagnostic accuracy of sputum for detecting lower respiratory tract infections using metagenomic next-generation sequencing. Paired sputum and bronchoalveolar lavage fluid samples were obtained from 68 patients; pathogens were detected in 67 sputum samples and 64 bronchoalveolar lavage fluid samples by metagenomic next-generation sequencing, respectively. The combined pathogen-detection rates in the sputum and bronchoalveolar lavage fluid samples were 80.90% and 66.2%, respectively. For sputum, the positive predictive values (PPVs) and negative predictive values (NPVs) for detecting bacteria were 0.72 and 0.73, respectively, with poor Kappa agreement (0.30; 95% confidence interval: 0.218-0.578, P < 0.001). However, viral detection in sputum had good sensitivity (0.87), fair specificity (0.57), and moderate Kappa agreement (0.46; 95% confidence interval: 0.231-0.693, P < 0.001). The PPVs and NPVs for viral detection in sputum were 0.82 and 0.67, respectively. The consistency between the sputum and bronchoalveolar lavage fluid was poor for bacterial detection yet moderate for viral detection. Thus, clinicians should be cautious when interpreting the results of sputum in suspected cases of lower respiratory tract infections, particularly with regards to bacterial detection in sputum. Viral detection in sputum appears to be more reliable; however, clinicians must still use comprehensive clinical judgment.

Recent advances in shelf life prediction models for monitoring food quality.

Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.

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In this study, we investigated the effects of long-term continuous cucumber cropping on phenolic acids in rhizosphere soil, as well as their link to soil chemical characteristics, enzyme activities, and microbiological activities, using rhizosphere soil from the 2nd, 6th, 10th, 14th, 18th, 20th, 24th, and 26th round of cucumber cultivation in solar greenhouse. The results showed that contents of phenolic acids increased significantly with increasing continuous cropping rounds. The increase amount per round of total phenolic acid was significantly higher in the early stage (0-2 rounds) and late stage (20-26 rounds) than middle stage (10-14 rounds) of continuous cropping. Soil nutrient contents were enriched, while invertase enzyme activity and microbial activities were decreased. Redundancy analysis showed that organic matter, total phosphorus, total nitrogen, available nitrogen, microbial biomass carbon and microbial metabolic entropy were main soil fertility factors correlating with the accumulation of phenolic acids. Results of structural equation model showed that soil phosphorus enrichment directly led to the accumulation of phenolic acids, and that nitrogen enrichment indirectly facilitated the accumulation of phenolic acids by altering the activity of microorganisms. As a result, proper nitrogen and phosphorus fertilizers application would reduce the accumulation of phenolic acids and alleviate the cucumber continuous cropping obstacles.

A vaccine targeting resistant tumours by dual T cell plus NK cell attack.

Most cancer vaccines target peptide antigens, necessitating personalization owing to the vast inter-individual diversity in major histocompatibility complex (MHC) molecules that present peptides to T cells. Furthermore, tumours frequently escape T cell-mediated immunity through mechanisms that interfere with peptide presentation1. Here we report a cancer vaccine that induces a coordinated attack by diverse T cell and natural killer (NK) cell populations. The vaccine targets the MICA and MICB (MICA/B) stress proteins expressed by many human cancers as a result of DNA damage2. MICA/B serve as ligands for the activating NKG2D receptor on T cells and NK cells, but tumours evade immune recognition by proteolytic MICA/B cleavage3,4. Vaccine-induced antibodies increase the density of MICA/B proteins on the surface of tumour cells by inhibiting proteolytic shedding, enhance presentation of tumour antigens by dendritic cells to T cells and augment the cytotoxic function of NK cells. Notably, this vaccine maintains efficacy against MHC class I-deficient tumours resistant to cytotoxic T cells through the coordinated action of NK cells and CD4+ T cells. The vaccine is also efficacious in a clinically important setting: immunization following surgical removal of primary, highly metastatic tumours inhibits the later outgrowth of metastases. This vaccine design enables protective immunity even against tumours with common escape mutations.

Single-cell nuclear architecture across cell types in the mouse brain.

Diverse cell types in tissues have distinct gene expression programs, chromatin states, and nuclear architectures. To correlate such multimodal information across thousands of single cells in mouse brain tissue sections, we use integrated spatial genomics, imaging thousands of genomic loci along with RNAs and epigenetic markers simultaneously in individual cells. We reveal that cell type­specific association and scaffolding of DNA loci around nuclear bodies organize the nuclear architecture and correlate with differential expression levels in different cell types. At the submegabase level, active and inactive X chromosomes access similar domain structures in single cells despite distinct epigenetic and expression states. This work represents a major step forward in linking single-cell three-dimensional nuclear architecture, gene expression, and epigenetic modifications in a native tissue context.

Integrated analysis of multimodal single-cell data.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Integrated spatial genomics reveals global architecture of single nuclei.

Identifying the relationships between chromosome structures, nuclear bodies, chromatin states and gene expression is an overarching goal of nuclear-organization studies1-4. Because individual cells appear to be highly variable at all these levels5, it is essential to map different modalities in the same cells. Here we report the imaging of 3,660 chromosomal loci in single mouse embryonic stem (ES) cells using DNA seqFISH+, along with 17 chromatin marks and subnuclear structures by sequential immunofluorescence and the expression profile of 70 RNAs. Many loci were invariably associated with immunofluorescence marks in single mouse ES cells. These loci form 'fixed points' in the nuclear organizations of single cells and often appear on the surfaces of nuclear bodies and zones defined by combinatorial chromatin marks. Furthermore, highly expressed genes appear to be pre-positioned to active nuclear zones, independent of bursting dynamics in single cells. Our analysis also uncovered several distinct mouse ES cell subpopulations with characteristic combinatorial chromatin states. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3 trimethylation at lysine 27 (H3K27me3) and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. This seqFISH+-based spatial multimodal approach can be used to explore nuclear organization and cell states in diverse biological systems.

Creation of virtual channels in the retina using synchronous and asynchronous stimulation-a modelling study.

Objective.The spatial resolution of an implantable neural stimulator can be improved by creation of virtual channels (VCs). VCs are commonly achieved through synchronized stimulation of multiple electrodes. It remains unknown whether asynchronous stimulation is able to generate comparable VC performance in retinal stimulation, and how VC can be optimized by re-designing stimulation settings. This study begins with exploring the feasibility of creating VCs using synchronous and asynchronous epiretinal stimulation, and ending with predicting the possible VC performance with a thorough exploration of stimulation parameter space.Approach.A computational model of epiretinal dual-electrode stimulation is developed to simulate the neural activity of a population of retinal ganglion cells (RGCs) under both synchronous and asynchronous stimulation conditions. The interaction between the electrode and RGCs under a range of stimulation parameters are simulated.Main results.Our simulation based on direct RGC activation suggests that VCs can be created using asynchronous stimulation. Two VC performance measures: 1) linearity in the change in centroid location of activated RGC populations, and 2) consistency in the size of activated RGC populations, have comparable performance under asynchronous and synchronous stimulation with appropriately selected stimulation parameters.Significance.Our findings support the possibility of creating VCs by directly activating RGCs under synchronous and asynchronous stimulation conditions. This study establishes the fundamental capability of VC creation based on temporal interactions within the RGC population alone and does not include the effects of potential indirect activation of any surviving inner retinal network neurons. Our results provide theoretical evidence for designing next-generation retinal prosthesis with higher spatial resolution.

Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics.

Despite rapid developments in single cell sequencing, sample-specific batch effects, detection of cell multiplets, and experimental costs remain outstanding challenges. Here, we introduce Cell Hashing, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its original sample, robustly identify cross-sample multiplets, and "super-load" commercial droplet-based systems for significant cost reduction. We validate our approach using a complementary genetic approach and demonstrate how hashing can generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

Molecular transitions in early progenitors during human cord blood hematopoiesis.

Hematopoietic stem cells (HSCs) give rise to diverse cell types in the blood system, yet our molecular understanding of the early trajectories that generate this enormous diversity in humans remains incomplete. Here, we leverage Drop-seq, a massively parallel single-cell RNA sequencing (scRNA-seq) approach, to individually profile 20,000 progenitor cells from human cord blood, without prior enrichment or depletion for individual lineages based on surface markers. Our data reveal a transcriptional compendium of progenitor states in human cord blood, representing four committed lineages downstream from HSC, alongside the transcriptional dynamics underlying fate commitment. We identify intermediate stages that simultaneously co-express "primed" programs for multiple downstream lineages, and also observe striking heterogeneity in the early molecular transitions between myeloid subsets. Integrating our data with a recently published scRNA-seq dataset from human bone marrow, we illustrate the molecular similarity between these two commonly used systems and further explore the chromatin dynamics of "primed" transcriptional programs based on ATAC-seq. Finally, we demonstrate that Drop-seq data can be utilized to identify new heterogeneous surface markers of cell state that correlate with functional output.

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.

Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C+ subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease.

Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs.

In humans, conventional dendritic cells (cDCs) exist as two unique populations characterized by expression of CD1c and CD141. cDCs arise from increasingly restricted but well-defined bone marrow progenitors that include the common DC progenitor that differentiates into the pre-cDC, which is the direct precursor of cDCs. In this study, we show that pre-cDCs in humans are heterogeneous, consisting of two distinct populations of precursors that are precommitted to become either CD1c+ or CD141+ cDCs. The two groups of lineage-primed precursors can be distinguished based on differential expression of CD172a. Both subpopulations of pre-cDCs arise in the adult bone marrow and can be found in cord blood and adult peripheral blood. Gene expression analysis revealed that CD172a+ and CD172a- pre-cDCs represent developmentally discrete populations that differentially express lineage-restricted transcription factors. A clinical trial of Flt3L injection revealed that this cytokine increases the number of both CD172a- and CD172a+ pre-cDCs in human peripheral blood.